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A Least Squares Regression Realised Covariation Estimation

Research output: Working paper

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A Least Squares Regression Realised Covariation Estimation. / Nolte, Ingmar; Vasios, Michalis; Voev, Valeri et al.
SSRN Working Paper, 2019.

Research output: Working paper

Harvard

Nolte, I, Vasios, M, Voev, V & Xu, Q 2019 'A Least Squares Regression Realised Covariation Estimation' SSRN Working Paper. https://doi.org/10.2139/ssrn.2205033

APA

Nolte, I., Vasios, M., Voev, V., & Xu, Q. (2019). A Least Squares Regression Realised Covariation Estimation. SSRN Working Paper. https://doi.org/10.2139/ssrn.2205033

Vancouver

Nolte I, Vasios M, Voev V, Xu Q. A Least Squares Regression Realised Covariation Estimation. SSRN Working Paper. 2019 Oct 3. doi: 10.2139/ssrn.2205033

Author

Nolte, Ingmar ; Vasios, Michalis ; Voev, Valeri et al. / A Least Squares Regression Realised Covariation Estimation. SSRN Working Paper, 2019.

Bibtex

@techreport{98caf1f9b7d04330a174caad547d8b0a,
title = "A Least Squares Regression Realised Covariation Estimation",
abstract = "We propose a least squares regression framework for the estimation of the realized covariation matrix using high frequency data. The new estimator is robust to market microstructure noise (MMS) and non-synchronous trading. Comprehensive simulation and empirical analysis show that our estimator performs as well as a set of popular estimators in the literature. More importantly, our framework allows for the unique identification of MMS noise moments. We find that these noise moments are related to measures of liquidity and contain predictive information that helps to significantly improve out-of-sample asset allocation.",
author = "Ingmar Nolte and Michalis Vasios and Valeri Voev and Qi Xu",
year = "2019",
month = oct,
day = "3",
doi = "10.2139/ssrn.2205033",
language = "English",
publisher = "SSRN Working Paper",
type = "WorkingPaper",
institution = "SSRN Working Paper",

}

RIS

TY - UNPB

T1 - A Least Squares Regression Realised Covariation Estimation

AU - Nolte, Ingmar

AU - Vasios, Michalis

AU - Voev, Valeri

AU - Xu, Qi

PY - 2019/10/3

Y1 - 2019/10/3

N2 - We propose a least squares regression framework for the estimation of the realized covariation matrix using high frequency data. The new estimator is robust to market microstructure noise (MMS) and non-synchronous trading. Comprehensive simulation and empirical analysis show that our estimator performs as well as a set of popular estimators in the literature. More importantly, our framework allows for the unique identification of MMS noise moments. We find that these noise moments are related to measures of liquidity and contain predictive information that helps to significantly improve out-of-sample asset allocation.

AB - We propose a least squares regression framework for the estimation of the realized covariation matrix using high frequency data. The new estimator is robust to market microstructure noise (MMS) and non-synchronous trading. Comprehensive simulation and empirical analysis show that our estimator performs as well as a set of popular estimators in the literature. More importantly, our framework allows for the unique identification of MMS noise moments. We find that these noise moments are related to measures of liquidity and contain predictive information that helps to significantly improve out-of-sample asset allocation.

U2 - 10.2139/ssrn.2205033

DO - 10.2139/ssrn.2205033

M3 - Working paper

BT - A Least Squares Regression Realised Covariation Estimation

PB - SSRN Working Paper

ER -